Postgres Pro
Downloads
Home   >   mailing lists

Re: Random Page Cost and Planner - Mailing list pgsql-performance

From Bryan Hinton
Subject Re: Random Page Cost and Planner
Date
Msg-id AANLkTilLahnsIGdmBYjs7Q7uuCspoRXRg6cEGA18PDPx@mail.gmail.com
Whole thread Raw
In response to Re: Random Page Cost and Planner  (David Jarvis <thangalin@gmail.com>)
Responses Re: Random Page Cost and Planner
List pgsql-performance
Tree view
Agree with Tom on his point about avoidance of cost param adjustments to fit specific test cases.
A few suggestions...as I assume you own this database...
- check out pg_statio_user_tables - optimize your cache hit ratio on blocks read...different time durations... pg_stat_bgwriter (read from a script or something and snapshot)
- pg_buffercache in contrib/  
- /proc/meminfo on linux 
- find out exactly what is going on with your kernel buffer cache (size, how it is buffering) and if your controller or drive is using a read ahead cache.  
- might want to play around with partial indexes vs. and/or range partitioning with exclusion constraints, etc.
- define I/O characteristics of the dataset - taking into account index clustering and index order on in-memory pages (i.e. re-cluster?), why need for multiple index if clustering indexes on heap?
- solidify the referential integrity constraints between those tables, on paper....define the use cases before modifying the database tables...i assume this is a dev database
- linux fs mount options to explore - i.e. noatime, writeback, etc.
-maybe look at prepared statements if you are running alot of similar queries from a single session? assuming web front end for your db - with say frequently queried region/category/dates for large read-only dataset with multiple join conditions?

There are some good presentations on pgcon.org from PGCon 2010 that was held last week...

If you take everything into account and model it correctly (not too loose, not too tight), your solution will be reusable and will save time and hardware expenses.

Regards - 

Bryan 



On Thu, May 27, 2010 at 2:43 AM, David Jarvis <thangalin@gmail.com> wrote:
Hi, Bryan.

I was just about to reply to the thread, thanks for asking. Clustering was key. After rebooting the machine (just to make sure absolutely nothing was cached), I immediately ran a report on Toronto: 5.25 seconds!

Here's what I did:
  1. Created a new set of tables that matched the old set, with statistics of 1000 on the station and taken (date) columns.
  2. Inserted the data from the old hierarchy into the new set, ordered by station id then by date (same seven child tables as before: one per category).
    • I wanted to ensure a strong correlation between primary key and station id.
  3. Added three indexes per table: (a) station id; (b) date taken; and (c) station-taken-category.
  4. Set the station-taken-category index as CLUSTER.
  5. Vacuumed the new tables.
  6. Dropped the old tables.
  7. Set the following configuration values:
    • shared_buffers = 1GB
    • temp_buffers = 32MB
    • work_mem = 32MB
    • maintenance_work_mem = 64MB
  • seq_page_cost = 1.0
  • random_page_cost = 2.0
  • cpu_index_tuple_cost = 0.001
  • effective_cache_size = 512MB
I ran a few more reports (no reboots, but reading vastly different data sets):
  • Vancouver: 4.2s
  • Yellowknife: 1.7s
  • Montreal: 6.5s
  • Trois-Riviers: 2.8s
No full table scans. I imagine some indexes are not strictly necessary and will test to see which can be removed (my guess: the station and taken indexes). The problem was that the station ids were scattered and so PostgreSQL presumed a full table scan would be faster.

Physically ordering the data by station ids triggers index use every time.

Next week's hardware upgrade should halve those times -- unless anyone has further suggestions to squeeze more performance out of PG. ;-)

Dave


pgsql-performance by date:

Previous
From: Craig James
Date:
Subject: Re: Does FILTER in SEQSCAN short-circuit AND?
Next
From: Tom Lane
Date:
Subject: Re: merge join killing performance